Atmospheric Infrared Sounder Research Articles

Intercomparisons of the Latest Precipitation Estimates from Satellite, Reanalysis, and Merged Products over Alaska

Abstract This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.

Mesoscale air motion and thermodynamics predict heavy hourly U.S. precipitation

Predicting heavy precipitation remains scientifically challenging. Here we combine Atmospheric Infrared Sounder (AIRS) temperature and moisture soundings and weather forecast winds to predict the formation of thermodynamic conditions favourable for convection in the hours following satellite overpasses. Here we treat AIRS retrievals as air parcels that are moved adiabatically to generate time-varying fields. Over much of the Central-Eastern Continental U.S. during the non-winter months of 2019–2020, our derived convective available potential energy alone predicts intense precipitation. For hourly precipitation above the all-hours 99.9th percentile, performance is marginally lower than forecasts from a convection permitting model, but similar to the ERA5 reanalysis and substantially better than using the original AIRS soundings. Our results illustrate how mesoscale advection is a major contributor to developing heavy precipitation in the region. Enhancing the full AIRS record as described here would provide an alternative approach to quantify multi-decade trends in heavy precipitation risk.

Influence of air temperature and interrelationship with greenhouse gases (CO2 and CH4) over Iraq using AIRS data

Global and regional observations of air temperature (AT) and specific atmospheric greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2) are required for a variety of applications, including constraining global or regional estimates of their significant impacts on the climate system. The present study employs Atmospheric Infrared Sounder (AIRS) -Level3 monthly products for AT, CH4, and CO2, at two standard pressure levels (925 and 500 hPa) over Iraq during 2010–2016. Both CO2 and CH4 shows significant seasonal variation, with maximum (minimum) CO2 observed in June (October), while CH4 recorded three maximum peaks during April, August, and November, and a minimum in February. CH4 shows a negative correlation during winter (DJF), spring (MAM), summer (JJA), and autumn (SON) with correlation coefficients (R) −0.627, −0.734, −0.491, and −0.688, respectively. The P-value is below 0.05 (4.14 × 10−15, 2.13 × 10−22, 1.1 × 10−8, and 5.2 × 10−19) for the four seasons, indicating a negative linear relationship. CO2 shows a low negative correlation in DJF and SON, and a low positive correlation in MAM and JJA seasons, with R values equal to −0.315, −0.221, 0.059, and 0.079, for DJF, SON, MAM and JJA seasons, respectively. The P-value was greater than 0.05 (0.061, 0.728, 0.647, and 0.195) for the four seasons, respectively, indicating a nonlinear relationship with AT. The monthly averaged time-series for CH4 and CO2 shows an evident increase, with an annual average increase of 1.81% (4.75) ppbv/year and 3.31% (1.84) ppm/year, respectively. Analysis reveals that the major sink and sources for CH4 are the presence of hydroxyl (OH) radicals and vegetation, whereas the major sources for CO2 are anthropogenic emissions, burning fossil fuels, and land-use change. The satellite observations of AIRS can efficiently show the spatiotemporal variations of air temperature versus CH4 and CO2 for the study area.

Construction of a Fine Extraction Process for Seismic Methane Anomalies Based on Remote Sensing: The Case of the 6 February 2023, Türkiye–Syria Earthquake

Identifying seismic CH4 anomalies via remote sensing has been verified as a legitimate method. However, there are still some problems, such as unknown reliability due to the complex characteristics of seismic anomalies. In this study, a multi-dimensional and multi-scale methane seismic anomaly extraction process for remote sensing was constructed with the Robust Satellite Technique (RST) based on the Atmospheric Infrared Sounder (AIRS) CH4 data and then applied to the 2023 Türkiye–Syria earthquake. This study obtained the two-dimensional temporal–spatial distribution of methane anomalies and temporal variation in the anomaly index. Based on this, the three-dimensional profile structure of the 8-day methane anomaly was extracted to determine the reliability of the anomaly. Finally, based on the daily methane anomaly, combined with atmospheric circulation and backward trajectory analysis as auxiliary tools, the influence of air mass migration was excluded to enhance the accuracy of CH4 anomaly determination. The results show that the three-dimensional anomalous structure is consistent with the geological characteristics of tectonic activities, and it appears as a “pyramid” or “inverted pyramid” type in a three-dimensional space. The anomalies caused by air mass migration can be eliminated by combining them with synoptic-scale circulation motion. The time series calculated at the epicenter or a certain point in a region may not accurately reflect the influence of regional or specific tectonic activity in the atmosphere. Thus, the optimal determination of the range and magnitude of atmospheric anomalies caused by tectonic activities is a difficult task for future research.

Assessing the Tropospheric Temperature and Humidity Simulations in CMIP3/5/6 Models Using the AIRS Obs4MIPs V2.1 Data

AbstractIn this study, the Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) V2.1 tropospheric air temperature, specific humidity, and relative humidity data are utilized to evaluate the global tropospheric temperature and humidity simulations in the fully coupled global climate models from the Coupled Model Intercomparison Project phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6), and possible simulation improvement in CMIP6 models in comparison to CMIP3 and CMIP5 models. Our analyses indicate that all three phases of CMIP models share similar tropospheric air temperature, specific humidity, and relative humidity biases in their multi‐model ensemble means relative to AIRS. Cold biases up to 4 K and positive relative humidity biases up to 20% are found in the free troposphere almost globally with maxima over the mid‐latitude storm tracks. Warm biases up to 2 K are seen over the Southern Ocean in the lower troposphere. Positive specific and relative humidity biases exist over the off‐equatorial oceans while negative specific and relative humidity biases are seen near the equator in the tropical free troposphere, which are related to the double‐intertropical convergence zone bias in the models. Both the air temperature and specific humidity biases are important to the relative humidity biases except in the tropical free troposphere where the specific humidity biases dominate. The tropospheric air temperature, specific humidity, and relative humidity biases are reduced from CMIP3 to CMIP5 and to CMIP6 at almost all pressure levels except at 300 hPa for specific humidity and in the boundary layer for relative humidity.

Assessments of Arctic Cloud Vertical Structure From AIRS Using Radar‐Lidar Observations

AbstractThe Atmospheric InfraRed Sounder (AIRS) aboard Aqua provides essential long‐term data on vertical cloud fraction, particularly valuable in the Arctic region. This study offers a comprehensive assessment of Arctic vertical cloud fraction derived from AIRS through a comparison with independent ground‐ and space‐based radar and lidar observations. In comparison to the measurements at the North Slope Alaska site, results reveal a significant underestimation of low‐level cloud cover by AIRS, especially for near‐surface clouds, while mid‐ and high‐level cloud fractions show better consistency. In comparison to the satellite‐based product from 3S‐GEOPROF‐COMB, the accuracy varies across different underlying surfaces (land vs. sea) and seasons. AIRS shows significant positive biases in mid‐level cloud fraction over sea surfaces with sea ice concentration below 15%, indicating potential limitations in the cloud retrieval algorithm in regions with large sea ice variations. The issue of low‐level clouds identification is primarily caused by the limited penetrating capability of infrared hyperspectral sensing and the accuracy of preceding surface and atmospheric state products, which diminish the accuracy of AIRS low‐level cloud fraction.

AIRS and MODIS Satellite-Based Assessment of Air Pollution in Southwestern China: Impact of Stratospheric Intrusions and Cross-Border Transport of Biomass Burning

The exacerbation of air pollution during spring in Yunnan province, China, has attracted widespread attention. However, many studies have focused solely on the impacts of anthropogenic emissions while ignoring the role of natural processes. This study used satellite data spanning 21 years from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to reveal two natural processes closely related to springtime ozone (O3) and PM2.5 pollution: stratospheric intrusions (SIs) and cross-border transport of biomass burning (BB). We aimed to assess the mechanisms through which SIs and cross-border BB transport influence O3 and PM2.5 pollution in Southwestern China during the spring. The unique geographical conditions and prevalent southwest winds are considered the key driving factors for SIs and cross-border BB transport. Frequent tropopause folding provides favorable dynamic conditions for SIs in the upper troposphere. In the lower troposphere, the distribution patterns of O3 and stratospheric O3 tracer (O3S) are similar to the terrain, indicating that O3 is more likely to reach the surface with increasing altitude. Using stratospheric tracer tagging methods, we quantified the contributions of SIs to surface O3, ranging from 6 to 31 ppbv and accounting for 10–38% of surface O3 levels. Additionally, as Yunnan is located downwind of Myanmar and has complex terrain, it provides favorable conditions for PM2.5 and O3 generation from cross-border BB transport. The decreasing terrain distribution from north to south in Yunnan facilitates PM2.5 transport to lower-elevation border cities, whereas higher-elevation cities hinder PM2.5 transport, leading to spatial heterogeneity in PM2.5. This study provides scientific support for elucidating the two key processes governing springtime PM2.5 and O3 pollution in Yunnan, SIs and cross-border BB transport, and can assist policymakers in formulating optimal emission reduction strategies.

Analysis of Seismic Methane Anomalies at the Multi-Spatial and Temporal Scales

Relevant studies have shown that methane gas has a close relationship with seismic activity. The concentration of methane released within a tectonic zone can reflect the intensity status of tectonic activities, which is important for seismic monitoring. In this study, the January 2020 Xinjiang Jiashi earthquake was taken as the research object, and the mature Robust Satellite Technique (RST) algorithm was used to characterize the L3-level methane product data from the hyperspectral sensor, Atmospheric Infrared Sounder (AIRS), installed on the Earth Observing System (EOS) AQUA satellite at the monthly scale, 8-day scale and daily scale. An analysis of the spatial and temporal distribution of methane was carried out for before and after the earthquake based on the 3D structural condition of the gas, and the 3D structural conditions of the 8-day scale were introduced. An 8-day scale 3D structural condition was introduced and migration validation was performed, and the results showed that (1) the seismic methane anomaly-extraction process proposed in this study is feasible; (2) the 3D contour features indicated that the methane anomalies that occurred before the Jiashi earthquake were caused by geogenic emissions; (3) the anomaly-extraction algorithm from this study did not extract the corresponding anomalies in the non-seismic year, which indicated that the anomaly-extraction algorithm of this study has some degree of feasibility; and (4) the migrated validation of the Wenchuan earthquake of May 2008 further suggested that methane anomalies at the time of the Wenchuan earthquake were caused by the earthquake.

Investigating the convective transport possibilities of lower-atmospheric pollutants to the UTLS region using rainwater and aerosol chemical characterization

Various attempts using different platforms to understand the chemical composition of the Asian Tropopause Aerosol Layer (ATAL) have concluded that it primarily consists of nitrate (NO3−) aerosols. Recent in-situ measurements from aircraft flying through ATAL have suggested that ammonia (NH3) pollution from Asia could serve as the precursor for nitrate aerosols in ATAL through gas-to-particle formation after undergoing convective uplift to the Upper Troposphere and Lower Stratosphere (UTLS) regions. In this study, we aimed to investigate the potential pathways of convective transport of lower-atmospheric pollutants by analysing the chemical composition of rainwater and aerosols in a highly convective region, Kolkata (Head Bay of Bengal), India. We utilized the PILS-IC system established at Kolkata Camp Observatory of the National Atmospheric Research Laboratory (KCON). The analysis revealed a significant dominance of NH4+ and NO3− ions in both rainwater and aerosol samples during the monsoon and post-monsoon seasons, with the dominance increasing during the post-monsoon season. Air mass trajectories indicated a clear influence of the Indo-Gangetic Plain (IGP) region during the post-monsoon season, which is well-known for its dense agricultural activities and industries. High concentrations of NH3 even at higher altitudes, have been observed in Atmospheric Infrared Sounder (AIRS) measurements. Moreover, using Outgoing Longwave Radiation (OLR) and vertical wind as proxies for tropical deep convection and updrafts/downdrafts, respectively, we found evidence supporting the possibility of convective transport of these lower-atmospheric pollutants to the UTLS regions during the monsoon season. In conclusion, this study provides evidence for the potential convective transport of lower-atmospheric pollutants, including NH3 and other precursor gases, to higher levels. These pollutants could partially serve as the source of nitrate aerosols in the ATAL through gas-to-particle formation processes.

Comparing Gravity Waves in a Kilometer‐Scale Run of the IFS to AIRS Satellite Observations and ERA5

AbstractAtmospheric gravity waves (GWs) impact the circulation and variability of the atmosphere. Sub‐grid scale GWs, which are too small to be resolved, are parameterized in weather and climate models. However, some models are now available at resolutions at which these waves become resolved and it is important to test whether these models do this correctly. In this study, a GW resolving run of the European Center for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), run with a 1.4 km average grid spacing (TCo7999 resolution), is compared to observations from the Atmospheric Infrared Sounder (AIRS) instrument, on NASA's Aqua satellite, to test how well the model resolves GWs that AIRS can observe. In this analysis, nighttime data are used from the first 10 days of November 2018 over part of Asia and surrounding regions. The IFS run is resampled with AIRS's observational filter using two different methods for comparison. The ECMWF ERA5 reanalysis is also resampled as AIRS, to allow for comparison of how the high resolution IFS run resolves GWs compared to a lower resolution model that uses GW drag parametrizations. Wave properties are found in AIRS and the resampled models using a multi‐dimensional S‐Transform method. Orographic GWs can be seen in similar locations at similar times in all three data sets. However, wave amplitudes and momentum fluxes in the resampled IFS run are found to be significantly lower than in the observations. This could be a result of horizontal and vertical wavelengths in the IFS run being underestimated.

Global total precipitable water variations and trends over the period 1958–2021

Abstract. Global responses of the hydrological cycle to climate change have been widely studied, but uncertainties still remain regarding water vapor responses to lower-tropospheric temperature. Here, we investigate the trends in global total precipitable water (TPW) and surface temperature from 1958 to 2021 using ERA5 and JRA-55 reanalysis datasets. We further validate these trends using radiosonde from 1979 to 2019 and Atmospheric Infrared Sounder (AIRS) and Special Sensor Microwave Imager/Sounder (SSMIS) observations from 2003 to 2021. Our results indicate a global increase in total precipitable water (TPW) of ∼ 2 % per decade from 1993–2021. These variations in TPW reflect the interactions of global warming feedback mechanisms across different spatial scales. Our results also revealed a significant near-surface temperature (T2 m) warming trend of ∼ 0.15 K decade−1 over the period 1958–2021. The consistent warming at a rate of ∼ 0.21 K decade−1 after 1993 corresponds to a strong water vapor response to temperature at a rate of 9.5 % K−1 globally, with land areas warming approximately twice as fast as the oceans. The relationship between TPW and T2 m showed a variation of around 6 % K−1–8 % K−1 in the 15–55° N latitude band, aligning with theoretical estimates from the Clausius–Clapeyron equation.

Volcanic clouds detection applying machine learning techniques to GNSS radio occultations

Volcanic clouds detection is a challenge especially when meteorological clouds are present in the same area. Several algorithms have been developed to detect and monitor volcanic clouds by using satellite instruments based on different remote sensing techniques. This work aims at classifying volcanic clouds based on atmospheric profiles retrieved by the GNSS (Global Navigation Satellite Systems) radio occultation technique. We collocated the radio occultations with the volcanic cloud detection from AIRS (Atmospheric InfraRed Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) for 11 big eruptions happening in the period 2008–2015 resulting in about 15000 profiles. We created an archive with the collocations and a corresponding number of profiles in “non-volcanic” environment in the same area and on the same period of the year. A support vector machine algorithm was applied to the archive in order to classify the clouds and to distinguish the volcanic clouds from the other types. The model performances are promising: the GNSS radio occultations are able to distinguish the volcanic clouds with an accuracy higher than 80% when the eruption occurs at high latitudes. The performances of the model are affected by the number of collocations used for the training. Nowadays, the number of radio occultations is higher than in the period considered in this research, making this work a pioneering study for a future operational product.

Estimation of Nighttime Aerosol Optical Depths Using Atmospheric Infrared Sounder Longwave Radiances

AbstractAerosol remote sensing typically relies on reflected shortwave radiation and thus lacks nighttime retrievals. Here we made an original attempt to retrieve nighttime aerosol optical depth (AOD) by utilizing longwave measurements in the atmospheric window region from the Atmospheric InfraRed Sounder (AIRS) instrument. A machine‐learning based algorithm is developed using AIRS longwave radiance and auxiliary data as the input and AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) as well as reanalysis surface temperature as the output. Independent validation indicates good agreement with lunar AOD derived from surface photometers. An overall increase in nighttime AOD compared to daytime is also uncovered, which is further corroborated by surface and space lidar measurements. The theoretical basis of the algorithm is further verified using radiative transfer simulations. Our approach substantially extends the potential of hyperspectral longwave measurements and offers valuable insights into nighttime aerosol properties.

Satellite-observed precipitation and total column water vapor

This study explores the relationship between water vapor and rainfall intensities over three tropical lands (Amazon Basin, Sahel, southern South America) and three tropical ocean regions (Atlantic Ocean, Indian Ocean, Niño 4). We utilized daily total column water vapor (TCWV) data from the Atmospheric Infrared Sounder (AIRS) and daily precipitation records from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation. Over tropical land, precipitation shows higher sensitivity to changes in water vapor, with a well-sorted pattern of an increased occurrence of higher daily precipitation as TCWV increases. Precipitation intensity over the Sahel, in particular, is extremely responsive to TCWV change. Over tropical oceans, the precipitation intensity is less sensitive to water vapor, particularly in the Indian Ocean and Niño 4 where precipitation intensities above the 40th percentile are no longer responding to the increasing TCWV. Quantifying water vapor and precipitation intensity aids in forecasting the occurrence of precipitation between tropical land and oceans.

Recreating Observed Convection‐Generated Gravity Waves From Weather Radar Observations via a Neural Network and a Dynamical Atmospheric Model

AbstractConvection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence.

A novel machine learning-based approach for improving global correction of AIRS-derived water vapor satellite product

Precise precipitable water vapor (PWV) observations play a crucially important role in weather and climate research. The Atmospheric Infrared Sounder (AIRS) on-board the Aqua spacecraft is a hyperspectral instrument that offers operational PWV products using infrared (IR) channels. However, the observational accuracy of AIRS-sensed cloudy-sky PWV products is much poorer than that of PWV retrievals under clear sky conditions. We present a new machine learning-based calibration model to improve the observational performance of operational AIRS-derived IR PWV satellite products under all sky conditions, which considers using several dependence elements correlated with remotely sensed IR PWV observations. The first-guess PWV estimates, obtained from the ERA5 reanalysis, are also utilized in the newly developed calibration approach. The ground-based GNSS-sensed PWV observations, collected in 2017 across the world, are used as the expected PWV output for the training of the newly proposed calibration model. The newly calibrated PWV data records considerably outperform operational AIRS-sensed PWV products, compared with independent PWV estimates from radiosonde observations during 2017–2020. The root-mean-square error of operational PWV satellite products decreases 19.05 % from 2.31 mm to 1.87 mm under cloud fraction (CF) = 0 condition (clear sky), 30.71 % from 3.68 mm to 2.55 mm under CF = (0,1] condition (cloudy sky), and 30.14 % from 3.55 mm to 2.48 mm under CF = [0,1] condition (all sky). The calibration method exhibits much higher RMSE reductions than previous approaches that do not utilize the first-guess PWV estimates, illustrating the effectiveness of our calibration approach. This research provides insights into calibrating satellite-sensed PWV estimates based on machine learning by jointly using ground-based and reanalysis-based PWV observations. The newly proposed calibration method has significant potential to calibrate operational PWV products from other satellite-borne sensors, in addition to the AIRS instrument.

Tropical cyclone “Vayu” generated gravity waves (20–60 min) in the mesosphere and lower thermosphere over Kolhapur

Tropical cyclones are one of the potential sources of gravity waves, which can transport energy and momentum to higher heights through their vertical propagation and interaction with the background flow and thereby influence the dynamics of the upper atmosphere. High-resolution wind data acquired by the Medium Frequency (MF) radar at Kolhapur (16.69°N, 74.24°E) are utilized to study the high frequency gravity waves (20–60 min) associated with the tropical cyclone named “Vayu” formed in the Indian Arabian Sea in June 2019. An enhancement of the gravity wave (GW) activities in the meridional wind is observed during 13–15 June 2019. The source of the gravity wave is the tropical Vayu cyclone of category 2 storm. We employed the gravity wave variance data from NASA's Atmospheric Infrared Sounder (AIRS) satellite for our analysis over stratospheric heights. The enhancement of gravity wave variance in both the stratosphere mesosphere heights and the low values of OLR indicate the strong convection associated with cyclone, the source of observed gravity waves. In the present study we analyzed the horizontal propagation direction of the cyclone generated gravity waves using perturbation ellipse, it is found to be in the north–south plane. The temperature profiles obtained from the SABER instrument on board TIMED satellite indicate the presence of mesospheric inversion layer with an amplitude of nearly 40 K on the day of large gravity wave variance (June 13, 2019). These results indicate the cyclone can generate gravity waves which can propagate to higher heights and modify the MLT thermal structure, providing evidence for the vertical coupling between lower and upper atmosphere.

New evidence for CH4 enhancement in the upper troposphere associated with the Asian summer monsoon

The Asian summer monsoon (ASM) region is a key region transporting air to the upper troposphere (UT), significantly influencing the distribution and concentration of trace gases, including methane (CH4), an important greenhouse gas. We investigate the seasonal enhancement of CH4 in the UT over the ASM region, utilizing retrievals from the Atmospheric Infrared Sounder (AIRS), model simulations and in-situ measurements. Both the AIRS data and model simulation reveal a substantial enhancement in CH4 concentrations within the active monsoon region of up to 3%, referring to the zonal means, and of up to 6% relative to the pre-monsoon season. Notably, the spatial distribution of the CH4 plume demonstrates a southwestward shift in the AIRS retrievals, in contrast to the model simulations, which predict a broader enhancement, including a significant increase to the east. A cross-comparison with in-situ measurements, including AirCore measurements over the Tibetan Plateau and airline sampling across the ASM anticyclone (ASMA), favors the enhancement represented by model simulation. Remarkable CH4 enhancement over the west Pacific is also evidenced by in-situ data and simulation as a dynamical extension of the ASMA. Our findings underscore the necessity for cautious interpretation of satellite-derived CH4 distributions, and highlight the critical role of in-situ data in anchoring the assimilation of CH4.

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible‐Near‐Infrared to Thermal Infrared

AbstractSuper‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR.

Fire monitoring and detection using brightness-temperature difference and water vapor emission from the atmospheric infrared sounder

Radiance data from the Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua satellite provide an opportunity for fire detection. As wildfires play an increasingly great role in the environments of the United States West Coast, emergency teams face an ever-challenging task of mitigation and prediction. Furthermore, the increasing rate of wildfires in the American West Coast places an ever-increasing strain on ecosystems and global climate. Of particular interest is the ability to create effective near real-time (NRT) imaging and prediction. Advances in this field can play a crucial role in assisting wildfire detection and monitoring. Typical sources for satellite fire imaging study are the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), the Advanced Very High Resolution Radiometer (AVHRR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments, but we present AIRS as complementary to these instruments with a new method of hotspot analysis. In this study, we propose a new method of fire detection by using AIRS's cloud-cleared radiance to detect hotspots and find new applications for this method of retrieval. We present the fire detection algorithm and initial assessments showing AIRS's ability to detect fires. In addition, we notice that there is more water vapor from the surface to the upper troposphere during the fire event as a result of biomass burning and an increase in air temperature.